Brain cancer is one of the most dominant causes of cancer death; the best way to diagnose and treat brain tumors is to screen early. Magnetic Resonance Imaging (MRI)is commonly used for brain tumor diagnosis; however, it is challenging problem to achieve higher accuracy and performance, which is a vital problem in most of the previous presented automated medical diagnosis. In this paper, we propose a Hybrid Two-Track U-Net(HTTU-Net) architecture for brain tumor segmentation. This architecture leverages the use of Leaky Relu activation and batch normalization. It includes two tracks; each one has a different number of layers and utilizes a different kernel size. Then, we merge these two tracks to generate the final segmentation. We use the focal loss, and generalized Dice (GDL), loss functions to address the problem of class imbalance. The proposed segmentation method was evaluated on the BraTS’2018 datasets and obtained a mean Dice similarity coefficient of 0.865 for the whole tumor region, 0.808 for the core region and 0.745 for the enhancement region and a median Dice similarity coefficient of 0.883, 0.895, and 0.815 for the whole tumor, core and enhancing region, respectively. The proposed HTTU-Net architecture is sufficient for the segmentation of brain tumors and achieves highly accurate results. Other quantitative and qualitative evaluation are discussed along the paper it confirms that our results are very comparable expert human-level performance, and could help experts to decrease the time of diagnostic.

This paper puts forth an aerial edge Internet-of-Things (EdgeIoT) system, where an unmanned aerial vehicle (UAV) is employed as a mobile edge server to process mission-critical computation tasks of ground Internet-of-Things (IoT) devices. When the UAV schedules an IoT device to offload its computation task, the tasks buffered at the other unselected devices could be outdated and have to be cancelled. We investigate a new joint optimization of UAV cruise control and task offloading allocation, which maximizes tasks offloaded to the UAV, subject to the IoT device’s computation capacity and battery budget, and the UAV’s speed limit. Since the optimization contains a large solution space while the instantaneous network states are unknown to the UAV, we propose a new deep graph-based reinforcement learning framework. An advantage actor-critic (A2C) structure is developed to train the real-time continuous actions of the UAV in terms of the flight speed, heading, and the offloading schedule of the IoT device. By exploring hidden representations resulting from the network feature correlation, our framework takes advantage of graph neural networks (GNN) to supervise the training of UAV’s actions in A2C. The proposed GNN-A2C framework is implemented with Google Tensorflow. The performance analysis shows that GNN-A2C achieves fast convergence and reduces considerably the task missing rate in aerial EdgeIoT.

Bruxism is a sleep disorder in which the patient clinches and gnashes their teeth. Bruxism detection using traditional methods is time-consuming, cumbersome, and expensive. Therefore, an automatic tool to detect this disorder will alleviate the doctor workload and give valuable help to patients. In this paper, we targeted this goal and designed an automatic method to detect bruxism from the physiological signals using a novel hybrid classifier. We began with data collection. Then, we performed the analysis of the physiological signals and the estimation of the power spectral density. After that, we designed the novel hybrid classifier to enable the detection of bruxism based on these data. The classification of the subjects into “healthy” or “bruxism” from the electroencephalogram channel (C4-A1) obtained a maximum specificity of 92% and an accuracy of 94%. Besides, the classification of the sleep stages such as the wake (w) stage and rapid eye movement (REM) stage from the electrocardiogram channel (ECG1-ECG2) obtained a maximum specificity of 86% and an accuracy of 95%. The combined bruxism classification and the sleep stages classification from the electroencephalogram channel (C4-P4) obtained a maximum specificity of 90% and an accuracy of 97%. The results show that more accurate bruxism detection is achieved by exploiting the electroencephalogram signal (C4-P4). The present work can be applied for home monitoring systems for bruxism detection.

Due to the COVID-19 pandemic, the world has faced a significant challenge in the increase of the rate of morbidity and mortality among people, particularly the elderly aged patients. The risk of acquiring infections may increase during the visit of patients to the hospitals. The utilisation of technology such as the “Internet of Things (IoT)” based on Fog Computing and Cloud Computing turned out to be efficient in enhancing the healthcare quality services for the patients. The present paper aims at gaining a better understanding and insights into the most effective and novel IoT-based applications such as Cloud Computing and Fog Computing and their implementations in the healthcare field. The research methodology employed the collection of the information from the databases such as PubMed, Google Scholar, MEDLINE, and Science Direct. There are five research articles selected after 2015 based on the inclusion and exclusion criteria set for the study. The findings of the studies included in this paper indicate that IoT-based Fog Computing and Cloud Computing increase the delivery of healthcare quality services to patients. The technology showed high efficiency in terms of convenience, reliability, safety, and cost-effectiveness. Future studies are required to incorporate the models that provided the best quality services using the Fog and Cloud Computation techniques for the different user requirements. Moreover, edge computing could be used to significantly enhance the provision of health services at home.

Automatic modulation classification is a task that is essentially required in many intelligent communication systems such as fibre‐optic, next‐generation 5G or 6G systems, cognitive radio as well as multimedia internet‐of‐things networks etc. Deep learning (DL) is a representation learning method that takes raw data and finds representations for different tasks such as classification and detection. DL techniques like Convolutional Neural Networks (CNNs) have a strong potential to process and analyse large chunks of data. In this work, we considered the problem of multiclass (eight classes) classification of modulated signals, which are, Binary Phase Shift Keying, Quadrature Phase Shift Keying, 16 and 64 Quadrature Amplitude Modulation corrupted by Additive White Gaussian Noise, Rician and Rayleigh fading channels using 3D‐CNN architectures in both frequency and spatial domains while deploying three approaches for data augmentation, which are, random zoomed in/out, random shift and random weak Gaussian blurring augmentation techniques with a cross‐validation (CV) based hyperparameter selection statistical approach. Simulation results testify the performance of 10‐fold CV without augmentation in the spatial domain to be the best while the worst performing method happens to be 10‐fold CV without augmentation in the frequency domain and we found learning in the spatial domain to be better than learning in the frequency domain.